AI policy sets a starting point. As tools, use cases, vendors and agents expand, an operating model must turn policy into repeatable ownership, review and evidence.
Define accountable ownership
Give one executive clear accountability, supported by business, technology, security, privacy, legal, procurement, HR and other relevant functions. Set decision rights proportionate to risk, without requiring approval for every low-risk activity.
Maintain an inventory
Inventory AI platforms, embedded features, use cases, vendors and agents, including data access and owners. Use it for review, reporting, customer assurance and incident response.
Classify use cases by risk
Use risk tiers to match review, documentation, testing and human oversight to impact. Employment, financial, customer and other consequential decisions warrant stronger controls than low-risk productivity uses.
Standardize intake and approvals
Define how teams request tools or use cases, who reviews them, required information and decision timelines. Vendor reviews should cover AI data handling, security, model behaviour, subcontractors and contract terms.
Integrate AI governance processes
Integrate AI into existing privacy, security architecture, third-party risk, records management, incident response, audit and change-management processes to keep governance scalable.
Measure both value and risk
Executive dashboards should track adoption, business outcomes, quality, exceptions, incidents, high-risk uses, vendor reviews and unresolved control actions.
Use frameworks as structure
Use NIST AI RMF and ISO/IEC 42001 to structure governance and management. Map relevant cybersecurity, privacy, contractual and sector requirements into the operating model.
Questions for your governance review
- Who has executive accountability for AI?
- Do we have an inventory of tools, use cases, vendors and data access?
- Which uses require elevated review?
- How do new AI requests move from idea to approval?
- What evidence would we provide to a major customer or auditor?
- Which metrics tell us whether AI is creating value and remaining within acceptable risk?
The objective is a governance system that can keep pace with adoption: clear ownership, proportionate decisions, evidence and regular review.